论文标题
图形神经网络和PDF文档中表提取的表示形式嵌入
Graph Neural Networks and Representation Embedding for Table Extraction in PDF Documents
论文作者
论文摘要
表可以在几种类型的文档中广泛使用，因为它们可以以结构化的方式带来重要的信息。在科学论文中，表可以总结新发现并总结实验结果，从而使研究可以与学者相提并论。几种方法执行了在文档图像上使用的表分析，从PDF文件转换过程中丢失了有用的信息，因为OCR工具可能容易出现识别错误，尤其是在表中的文本。这项工作的主要贡献是解决表提取问题，利用图形神经网络。节点特征富含适当设计的表示形式嵌入。这些表示形式不仅有助于更好地区分纸张的其他部分，还可以将表单元与桌子标题区分开。我们通过合并PublayNet和PubTables-1M数据集中提供的信息，在获得的新数据集上实验评估了所提出的方法。
Tables are widely used in several types of documents since they can bring important information in a structured way. In scientific papers, tables can sum up novel discoveries and summarize experimental results, making the research comparable and easily understandable by scholars. Several methods perform table analysis working on document images, losing useful information during the conversion from the PDF files since OCR tools can be prone to recognition errors, in particular for text inside tables. The main contribution of this work is to tackle the problem of table extraction, exploiting Graph Neural Networks. Node features are enriched with suitably designed representation embeddings. These representations help to better distinguish not only tables from the other parts of the paper, but also table cells from table headers. We experimentally evaluated the proposed approach on a new dataset obtained by merging the information provided in the PubLayNet and PubTables-1M datasets.
